ABSTRACT The overall goal of the annual Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) at the University of Washington is to educate the next generation of researchers in a broad range of state-of-the-art quantitative methods for infectious disease research. Courses for skill development: SISMID is a collection of 16 2.5-day modules offered over 2.5 weeks in July on a variety of topics relevant to research education in statistics, modeling and computational methods applied to infectious diseases. Most participants take on average three modules per year. SISMID has been held each summer since 2009. This proposal requests funds for 2020-2024. The 2020 SISMID proposes to offer the following: 1. Probability and Statistical Inference; 2. Mathematical Models of Infectious Diseases; 3. Introduction to R; 4. Causal Inference; 5. Evolutionary Dynamics and Molecular Epidemiology of Viruses; 6. Stochastic Epidemic Models with Inference; 7. Markov chain Monte Carlo I; 8. Microbiome Data Analysis; 9. Pathogen Evolution, Selection, and Immunity; 10. Simulation-based Inference for Epidemiologic Dynamics; 11. Statistics and Modeling with Novel Data Streams; 12. Infectious Diseases, Immunology, Within Host Models; 13. Markov chain Monte Carlo II for Infectious Diseases; 14. Spatial Statistics in Epidemiology and Public Health; 15. Contact Network Epidemiology: 16. Reconstructing Transmission with Genomic Data. The instructors are drawn from the University of Washington and other academic institutions in the USA and Europe, and industry. Instructor mentors will be assigned to recipients of support through this grant and put into email contact before SISMID. They will meet together during SISMID. Research experiences will include teams working on ongoing projects and using innovative methods for reproducible research. This NIAID Research Education Program (R25) will allow us to provide graduate students and postdoctoral fellows approximately 350 modules per year without charge and 100 partial travel awards.